时变相互作用的动态贝叶斯推断：从耦合振子对到振子网络
Dynamical Bayesian Inference of Time-evolving Interactions: From a Pair of Coupled Oscillators to Networks of Oscillators
摘要：
生命系统具有随时间演化的相互作用，直到最近，在存在噪声的情况下，还不能从记录的时间序列中准确识别。Stankovski等人（Phys.Rev.Lett. 109 024101，2012）介绍了一种基于动态贝叶斯推断的方法，该方法便于同时检测时变同步、影响的方向性和耦合函数。它可以区分非同步动态噪声引起的相位滑移。该方法是基于相位动态，与贝叶斯推断的时间演变的参数是通过成形的先验密度，以纳入知识的先前样本。我们现在使用数字生成的数据、来自模拟电子电路的数据和心肺数据来详细介绍该方法。我们还推广的方法，包括网络的相互作用的振荡器，从而证明其适用于小规模的网络。
英文摘要：
Living systems have time-evolving interactions that, until recently, could not be identified accurately from recorded time series in the presence of noise. Stankovski et al. (Phys. Rev. Lett. 109 024101, 2012) introduced a method based on dynamical Bayesian inference that facilitates the simultaneous detection of time-varying synchronization, directionality of influence, and coupling functions. It can distinguish unsynchronized dynamics from noise-induced phase slips. The method is based on phase dynamics, with Bayesian inference of the time- evolving parameters being achieved by shaping the prior densities to incorporate knowledge of previous samples. We now present the method in detail using numerically-generated data, data from an analog electronic circuit, and cardio-respiratory data. We also generalize the method to encompass networks of interacting oscillators and thus demonstrate its applicability to small-scale networks.
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